Abstract
Motion retargetting refers to the process of adapting the motion of a source character to a target. This paper presents a motion retargetting model based on temporal dilated convolutions. In an unsupervised manner, the model generates realistic motions for various humanoid characters. The retargetted motions not only preserve the high-frequency detail of the input motions but also produce natural and stable trajectories despite the skeleton size differences between the source and target. Extensive experiments are made using a 3D character motion dataset and a motion capture dataset. Both qualitative and quantitative comparisons against prior methods demonstrate the effectiveness and robustness of our method.
Original language | English |
---|---|
Pages (from-to) | 497-507 |
Number of pages | 11 |
Journal | Computer Graphics Forum |
Volume | 39 |
Issue number | 2 |
DOIs | |
Publication status | Published - 2020 May 1 |
Bibliographical note
Funding Information:This work was supported by the National Research Foundation of Korea (NRF) Grant funded by the Korea government (MSIT) (NRF‐2017M3C4A7066316 and No. NRF2016‐R1A2B3014319).
Funding Information:
This work was supported by the National Research Foundation of Korea (NRF) Grant funded by the Korea government (MSIT) (NRF-2017M3C4A7066316 and No. NRF2016-R1A2B3014319).
Publisher Copyright:
© 2020 The Author(s) Computer Graphics Forum © 2020 The Eurographics Association and John Wiley & Sons Ltd. Published by John Wiley & Sons Ltd.
Keywords
- CCS Concepts
- • Computing methodologies → Neural networks
ASJC Scopus subject areas
- Computer Graphics and Computer-Aided Design